PROJECT SUMMARY Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease that affects 1% of the adult world population, including 1.5 million adults in the U.S. There is currently no cure for RA. In addition to joint inflammation, the disease can lead to irreversible erosive damage to cartilage and bone, resulting in significant disability and poor quality of life. Since early treatment of RA is beneficial and results in a less severe disease course, it was hypothesized that very early stages of RA may represent a ?therapeutic window of opportunity? during which appropriate treatment may be able to alter the course of the disease, preventing further progression, and possibly could switch off the disease process. It has also been shown that it would be more beneficial to identify RA patients closer to the onset of symptoms for early therapy, i.e. when symptoms have been present for less than 3 months. At that time, patients may not even satisfy the criteria for early RA diagnosis but get a diagnosis of ?undifferentiated arthritis? (UA), which is the most common diagnosis when arthritis symptoms first appear and patients cannot be diagnosed with any specific disease. Hence, prediction models are needed that can accurately predict who among all UA patients will (a) be among the 30% who progress to RA, and hence, should be started on early aggressive treatment, and (b) will go into remission or will develop other conditions, and hence, should not unnecessarily be given aggressive RA treatment as these are associated with serious side effects. Currently available prediction models do not accurately predict which UA patients will progress to RA. Since genomic markers associated with early RA have not been thoroughly investigated and are not included among existing prediction models, we propose to first identify biomarkers of progression from UA to RA at the levels of gene expression and genetic polymorphisms (expression quantitative trait loci, eQTLs) in a cohort of early UA patients. The biomarkers identified will be included in a prediction model with high specificity and sensitivity that can be applied to accurately predict progression from UA to RA. The use of such a precision medicine approach for early RA treatment will provide significant health and cost benefits. Second, we also propose to investigate biological changes associated with progression from UA to RA over time to gain a better understanding of potential pathways that may be involved in RA pathogenesis.